In [14]:
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from scipy.ndimage import gaussian_filter
# Recalculate bins if necessary (increase for smoother contours)
nx = 160 # Double the number of longitude bins for finer resolution
ny = 80 # Double the number of latitude bins for finer resolution
# Assuming 'lats' and 'lons' variables are your latitude and longitude data points
# Recalculate the bins and density
lon_bins = np.linspace(-125, -66, nx+1) # +1 because these are bin edges
lat_bins = np.linspace(24, 50, ny+1) # +1 because these are bin edges
density, _, _ = np.histogram2d(lats, lons, [lat_bins, lon_bins])
# Create the map projection
projection = ccrs.AlbersEqualArea(central_longitude=lon_0, central_latitude=lat_0)
# Create figure and axis objects
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1, projection=projection)
# Set the extent of the map
ax.set_extent([-125, -66, 24, 50], crs=ccrs.PlateCarree())
# Add land, coastlines, and state features
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
ax.add_feature(cfeature.STATES.with_scale('50m'))
# Define the coordinate mesh for plotting
lon_bins_2d, lat_bins_2d = np.meshgrid(lon_bins[:-1], lat_bins[:-1])
# Plot the fire density data
density_smoothed = gaussian_filter(density, sigma=2) # Apply a Gaussian filter to smooth
fire_plot = ax.pcolormesh(lon_bins_2d, lat_bins_2d, density_smoothed,
transform=ccrs.PlateCarree(),
cmap='coolwarm', shading='auto')
# Add a colorbar
cbar = plt.colorbar(fire_plot, orientation='vertical', pad=0.02, aspect=50)
cbar.set_label('Fire Density')
# Define gridline options and label formatting
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.top_labels = False
gl.right_labels = False
gl.xlines = True
gl.ylines = True
gl.xlabel_style = {'size': 15, 'color': 'gray'}
gl.ylabel_style = {'size': 15, 'color': 'gray'}
plt.show()
